Satellite-Based Ice and Iceberg Monitoring for Offshore Engineering Design and Tactical Operations
Bibliographic record
Abstract
Abstract Characterization of the ice environment is an essential step in the probabilistic design approach of Arctic offshore structures. Uncertainty here could lead to overly conservative designs and higher than necessary cap-ex. Inclusion of an effective ice management strategy further mitigates risk and cost. Both tactical and historical knowledge of the ice environment can be achieved cost effectively using space-based surveillance or Earth Observation (EO). The mapping and monitoring of ice visited regions is a fundamental application area for EO, in particular Synthetic Aperture Radar (SAR) missions. It is an all-weather, day-and-night, geographically independent sensor. Spaceborne SAR mapping of ice has been available since the 1970s; however routine SAR monitoring was not possible until the launch of Europe's ERS-1 satellite in 1992. This event also heralded in an era of large scale archiving of radar data. In addition to chart data available through various national ice centres, there is now an archive of almost 20 years of raw satellite radar data that can be used to create highly detailed historical maps of ice and icebergs to aid in the design process. Over the past 5–10 years, the number of radar satellites has quadrupled and technical capabilities have increased by an order of magnitude. Weekly surveillance has been replaced with daily and performance metrics are approaching 100%. Satellites are now a reliable, effective tool for a large portion of a project's life cycle - from exploration, to developing a design basis to production. Its prevalence within the industry is growing. This paper will highlight advances in satellite monitoring, new pricing policies to increase uptake, and recent experience using satellite SAR operationally in northern oil and gas projects. Background In conducting safe and cost effective operations, ice management and risk mitigation practices are integral to operations. The first element of the ice management plan is detection of ice and icebergs. This provides a basis for all subsequent ice management actions such as towing and suspension of operations. Comprehensive explanations of the ice management process and technologies that can be used to facilitate an ice management plan were detailed by Randell et al. (2009). Satellite SAR is well suited to map and monitor icebergs and sea ice due to its ability to provide images day or night, through cloud or fog, independent of environmental conditions. Satellite SAR mapping of ice has been available since the 1970s, although routine SAR monitoring of ice was only made possible in the 1990s with the launch of the European satellite ERS-1 in 1992. This satellite also heralded in an era of large scale data archiving of radar data. In addition to data available through various national ice centres, there is an archive of almost 20 years of raw satellite radar data that can be used to create highly detailed historical maps of ice and icebergs to aid in the design process. Many existing and almost all of the new SAR satellites are "operations ready" in that they provide their data in Near-Real- Time (NRT) with imagery available via the internet within hours of acquisition. The next generation of SARs to be launched within the next few years are specifying imagery delivery times of less than one hour; an investment in a ground station facility can allow data provision in minutes of acquisition. With these capabilities, SAR can be used effectively by the industry, with particular effectiveness in northern resource development. The increasing prevalence of SAR, along with lower data costs and flexible data policies will lead to increased use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".